In recent years, artificial intelligence (AI) techniques have been developed rapidly. With the
help of big data, massive parallel computing, and optimization algorithms, machine learning …

This paper proposes a novel deep learning (DL) based source reconstruction method
(SRM). The proposed DL-based SRM employs the deep convolutional asymmetric encoder …

Inspired by a discretized formulation resulting from volume integral equation and method of
moments, we propose an electric flux density learning method (EFDLM) using cascaded …

Accurate predictions of near-field scattering of metal nanoparticles is an important mission in
modern computational optics. The traditional difference algorithm usually requires a lot of …

In this chapter, we introduce the approaches of applying deep learning techniques to
electromagnetic forward modeling. These approaches are divided into three types: fully data …

Inspired by a discretized formulation resulting from volume integral equation and method of
moments, we propose an electric flux density learning method (EFDLM) using cascaded …

In this study, we will utilize the PINNs method to explore and analyze the electromagnetic
scattering properties of cylinders with complex geometries. PINN is a physically constrained …

In this paper, we present physics-informed graph residual learning (PhiGRL) to model the
scattering of 3D PEC targets by solving combined field integral equations (CFIEs). Emulating …

This study applies physics-informed residual learning to compute electromagnetic scattering
by perfect electric conductors (PECs). The formulation is based on the combined field …